A smart acoustic information recognition-based welded weld impact quality determination method and system, comprising: controlling a tip of an ultrasonic impact gun (1) to perform impact treatment on a welded weld with different treatment pressures, treatment speeds, treatment angles and impact frequencies, obtaining acoustic signals during the impact treatment, calculating feature values of the acoustic signals, and constructing an acoustic signal sample set including various stress conditions; marking the acoustic signal sample set according to impact treatment quality assessment results for the welded weld; establishing a multi-weight neural network model, and using the marked acoustic signal sample set to train the multi-weight neural network model; obtaining feature values of welded weld impact treatment acoustic signals to be determined, inputting the feature values into the trained multi-weight neural network model, and outputting determination results for welded weld impact treatment quality to be determined.

The present disclosure relates to a metal machining apparatus having a gas nozzle for generating a gas jet. The apparatus also has a nozzle exit opening on one end on the outside; an electronic camera for acquiring a digital image of the end of the gas nozzle with the nozzle exit opening. The apparatus also includes a pattern recognition module for mapping the digital image to at least one nozzle pattern from the group of nozzle state and/or nozzle type.

The laser machining system includes a laser device configured to output a laser beam, and a machining head configured to emit the laser beam emitted by a laser oscillator of the laser device and propagated through an optical fiber, to a workpiece in order to perform laser machining. The machining head includes at least one wavelength selective mirror having wavelength selectivity with various values of reflectivity and transmittance according to wavelengths, and at least one image capturing device. The laser machining system monitors abnormality in a laser optical system leading from the laser oscillator to the machining head, during the laser machining, by reflecting light propagated from a side of introduction of the laser beam into the machining head by the wavelength selective mirror, making the light incident on an image capturing surface of the image capturing device, and detecting incident light illuminance distribution appearing on the image capturing surface of the image capturing device.

Systems and methods for real time feedback and for updating welding instructions for a welding robot in real time is described herein. The data of a workspace that includes a part to be welded can be received via at least one sensor. This data can be transformed into a point cloud data representing a three-dimensional surface of the part. A desired state indicative of a desired position of at least a portion of the welding robot with respect to the part can be identified. An estimated state indicative of an estimated position of at least the portion of the welding robot with respect to the part can be compared to the desired state. The welding instructions can be updated based on the comparison.

Disclosed herein are systems and methods for identifying welding anomalies and discontinuities in stud welding using AI models. Instead of conventional welding accuracy methods (e.g. destructive and/or image generation methods) a processor may communicate with one or more sensors associated with a joining machine to retrieve joining data and attributes. The processor may then execute an AI model that is trained based on previously performed stud welding, their corresponding welding attributes, and their corresponding discontinuities and/or anomalies. The processor may execute the AI model using data retrieved from the sensors and may calculate a likelihood of a discontinuity and discontinuity attributes, such as, location, depth, and the like. The processor may also execute a second AI model to identify an appropriate course of action to remedy the identified/predicted discontinuity.

According to an embodiment, an object tracking device in a welding process tracks and outputs a predetermined object in a welding image. The object tracking device comprises a camera device capturing the welding image including a base material and a welding torch for welding the base material, a controller receiving a plurality of camera control parameter-varied images from the camera device, identifying the predetermined object in the received images, and generating an object tracking image, the plurality of camera control parameter-varied images having varied camera control parameters of the camera device, and an output device outputting the welding image captured by the camera device, the plurality of images received by the controller, or the object tracking image generated by the controller.

According to an embodiment, an object tracking device in a welding process tracks and outputs a predetermined object in a welding image. The object tracking device comprises a camera device capturing the welding image including a base material and a welding torch for welding the base material, a controller receiving a plurality of camera control parameter-varied images from the camera device, identifying the predetermined object in the received images, and generating an object tracking image, the plurality of camera control parameter-varied images having varied camera control parameters of the camera device, and an output device outputting the welding image captured by the camera device, the plurality of images received by the controller, or the object tracking image generated by the controller.

A method for detecting welding defects of workpieces includes obtaining a first image from an image capturing element; extracting welding information from the first image; transmitting the welding information to a logic processing element to obtain welding defects; the logic processing element formed by evolution of self-learning of historical welding information; and generating display information of the welding defects and displaying visual form and/or characteristics values of the welding defects. An apparatus and a non-transitory computer readable medium for detecting welding defects of workpieces are also disclosed.

A solder joint inspection model training method includes the steps of: training a first identification model according to first sample images to identify a surface-mount device with a solder joint in an image; training a second identification model according to second sample images to identify a surface-mount device without a solder joint in an image; inputting labeled original images to a trained first identification model to output first images; inputting the first images to a trained second identification model to output second images; masking the first images with the second images to generate images with normal solder joints and images with abnormal solder joints; and training a solder joint inspection model based on the images with normal solder joints and the images with abnormal solder joints.

An adaptive welding system including a plurality of welding machines, an edge computing system configured to gather weld data from a first welding machine from the plurality of welding machines, and a weld analytics system configured to build an optimized operational model for the first welding machine based at least upon the weld data. The weld analytics system is further configured to transmit the optimized operational model to the first welding machine as a firmware image.